This invention relates to digital communication methods and systems and more particularly to methods and systems for decoding symbols from a signal that is received on a communications channel.
Digital communication systems and methods including digital wireless communication systems and methods are widely used to convey information. As is well known to those having skill in the art, in digital communications, information is often generated as, or translated into, a stream of bits. A transmitter then maps this bit stream into a symbol stream which is modulated and transmitted on a communications channel. A digital receiver detects the signal and maps the signal back into decoded bits.
As is also well known, equalizers are used to demodulate information symbols from information-modulated signals that are received from a transmitter through a communications channel suffering from time dispersion, giving rise to Inter-Symbol Interference (ISI). Conventional equalizers include transversal equalizers, which apply transversal or finite impulse response filters to the received signal which are the inverse of the channel characteristic, thus undoing at least some of the deleterious effect of the channel. Unfortunately, transversal equalizers may not be effective for all types of channels, and can result in magnification of noise.
Another known type of equalizer is a Decision Feedback Equalizer (DFE) wherein already decoded bits are used to subtract out their delayed echoes caused by time dispersion, thereby canceling ISI and enabling the next bit to be decoded. Unfortunately, DFE equalizers may only work well when later delayed echoes are weaker than the signal component being decoded. Otherwise, DFE may discard too much useful energy in the echoes. It is possible in such circumstances to store received signal samples and process them retrospectively in time reversed order. Equalizers that process in either a forward or reverse time order are described, for example, in U.S. Pat. Nos. 5,841,816 to coinventor Dent and Croft entitled xe2x80x9cDiversity PI/4-DQPSK Demodulationxe2x80x9d and U.S. Pat. No. 5,335,250 to coinventor Dent and Chennakeshu, entitled xe2x80x9cMethod and Apparatus for Bidirectional Demodulation of Digitally Modulated Signalsxe2x80x9d, the disclosures of which are hereby incorporated herein by reference in their entirety. These patents also describe a Maximum Likelihood Sequence Estimation (MLSE) technique which, unlike DFE, need not discard energy in delayed echoes.
Equalizers generally may need to determine the phase and magnitude of all significant delayed echoes relative to some nominal delayed ray, which values characterize the transmission channel and therefore are called channel coefficients or channel estimates. Channel estimates may be determined by including known symbols in the transmitted signal in the form of a xe2x80x9csyncwordxe2x80x9d or xe2x80x9cpilot symbolsxe2x80x9d. Alternatively, in the case of equalizers known as blind equalizers, the channel and the unknown data may be estimated simultaneously. One form of blind equalizer is described in U.S. Pat. No. 5,557,645 to coinventor Dent, entitled xe2x80x9cChannel-Independent Equalizer Devicexe2x80x9d the disclosure of which is hereby incorporated herein by reference in its entirety.
When an equalizer or demodulator is to be followed by an error correction decoder, it can be advantageous for error correction decoding to pass xe2x80x9csoftxe2x80x9d decisions rather than xe2x80x9chardxe2x80x9d decisions from the demodulator to the error correction decoder. The soft decisions should preferably be values indicative of the likelihood that a particular symbol is one or other value, and more specifically, preferably proportional to the negative logarithm of the symbol likelihood. Often, the signal-to-noise ratio of the symbol is an adequate soft decision. Such soft decisions may be obtained from MLSE equalizers by a technique described in U.S. Pat. No. 5,099,499 to Hammar, entitled xe2x80x9cMethod of Generating Quality Factors for Binary Digits Obtained in the Viterbi-Analysis of a Signalxe2x80x9d the disclosure of which is hereby incorporated herein by reference in its entirety. Hammar equates a soft decision for a binary symbol to a difference between a metric for the opposite value of the symbol minus the metric for the decoded value of the symbol, divided by the sum of all metrics, or final metric, of the MLSE equalizer. The difference metrics are indicative of the symbol""s signal strength while the final metric is indicative of the noise in the channel. Thus Hammar""s soft information can be a measure of signal-to-noise ratio on a per-symbol basis.
U.S. Pat. No. 5,331,666 to coinventor Dent, entitled xe2x80x9cAdaptive Maximum Likelihood Demodulatorxe2x80x9d the disclosure of which is hereby incorporated herein by reference in its entirety, describes an MLSE device for four-phase modulations such as QPSK, OQPSK, DQPSK and xcfx80/4-DQPSK. It describes how metric computations may be simplified by exploiting quadrantal symmetry in a 4-phase constellation.
In co-pending application Ser. No. 09/499,977, filed Feb. 8, 2000, entitled Methods, Receivers And Equalizers For 8PSK Modulation Having Increased Computational Efficiency to coinventor Zangi, an 8-PSK equalizer is described that exploits octant-symmetries in the constellation to reduce metric calculations. This application is hereby incorporated by reference herein in its entirety.
Unfortunately, the above-described equalization techniques may become computationally intensive when the number of symbols and/or the amount of intersymbol interference becomes large. The computational complexity may result in increased power consumption in a mobile terminal and/or increased processing time. Moreover, the symbol decisions that are made in an equalizer may not provide a desired input for an error correction decoder. Thus, in order to increase the probability that individual symbols are decoded with high probability, further complexity may be introduced.
In particular, for various reasons, symbol decisions made in an equalizer may not provide a desired input for an error correction decoder. An MLSE equalizer determines a sequence which, with highest probability, is the sequence that was transmitted. However, an individual symbol may not be the symbol which, with highest probability, was the symbol transmitted. To obtain the probability of individual symbols, a different type of demodulation known as Maximum A-Posteriori (MAP) decoding may be used, which however can be of excessive complexity. A difference between MAP and MLSE derives from the tendency of error events in an MLSE device to be multiple error events, in which a group of adjacent symbols may be erroneous or of high error probability.
Moreover, full MLSE often is too complex and a combination of MLSE and DFE may be used, known as Per-Survivor Processing (PSP). MLSE-PSP may be useful when the time dispersion spans a large number L of symbol periods, the symbols are from a larger than binary alphabet of M symbols, and the resulting full MLSE complexity, which is proportional to ML may be too large. For example, for 8-PSK (M=8) and five symbol periods of time dispersion, an MLSE device may need to compute 85=32,768 metrics per decoded symbol, which generally is excessive.
In U.S. application Ser. No. 09/237,356 filed Jan. 26, 1999 entitled xe2x80x9cReduced Complexity MLSE Equalizer for M-ARY Modulated Signalsxe2x80x9d to coinventor Zangi, the disclosure of which is hereby incorporated herein by reference in its entirety, it is shown that this number may be reduced by exploiting octant symmetries in 8-PSK. However, even reducing metric computations four or eightfold may not be enough. As a result, less than full MLSE may need to be used.
In reduced MLSE, not all combinations of five successive symbols (for 5-symbol time dispersion) are computed. The oldest (for example three) symbols are instead xe2x80x9cdecidedxe2x80x9d and their values used in a DFE operation to subtract out the ISI caused by the already decided symbols, leaving only, for example, two symbols to be hypothesized in the MLSE part of the device. This can reduce the number of metric computations to 64. Because different sets of the decided symbols are kept in a path history for each of the possibilities or states of the MLSE device, which number MLxe2x88x921, where L is the number of symbols estimated by MLSE, the DFE taps are xe2x80x9cper statexe2x80x9d DFE taps and not the same for all states. Hence the term xe2x80x9cper survivor processingxe2x80x9d is used.
Unfortunately, just as in pure DFE, MLSE-PSP may not operate well when the energy in delayed echoes corresponding to the DFE or PSP taps is greater than the energy in the MLSE taps. However, the distribution of echo energy between different symbol-spaced delays can be altered by filtering the received data to either cancel some echoes or add other echoes or both. Thus MLSE-PSP can be performed by first estimating the strengths of delayed echoes, then deciding if the energy distribution is favorable for MLSE-PSP or time-reversed MLSE-PSP. If not, for example there being more strong echoes than MLSE taps, a pre-filter is determined using, for example, the method described in the above-cited Zangi application, to change the energy distribution to be most favorable to MLSE-PSP. There may, however, be a reduction in performance from full MLSE, resulting in more errors and imperfect soft information, as a consequence of the pre-filtering operation, which does not represent a matched filter for the signal.
Accordingly, notwithstanding the above-described techniques, there continues to be a need for methods and systems for decoding multibit symbols that can accurately decode individual symbols with high accuracy, without the need to introduce undue processing complexity into the decoding methods and systems.
Multibit symbols from a signal that is received on a communications channel are decoded by combining a matched filtered signal that is derived from input signal samples, and hard decisions that are derived from the input signal samples, in order to obtain soft symbol values. The soft symbol values then may be processed to obtain soft bit values for each bit of the multibit symbols. In particular, the soft symbol values may then be converted to soft bit values and interleaved to provide soft, coded bit values. Error correction decoding then may be applied to the soft, coded bit values to provide the decoded bits.
More specifically, at least one sample for each symbol is obtained from the signal that is received from the communications channel. The at least one sample for each symbol is processed to obtain hard symbol decisions for the symbols. The at least one sample for each symbol also is matched filtered to obtain a block of matched filtered samples comprising one sample per symbol. The block of matched filtered samples and the hard symbol decisions then are combined to obtain soft symbol values. The soft symbol values then are processed to obtain soft bit values for the multibit symbols.
MLSE-PSP processing may be performed on prefiltered input signal samples to obtain hard decisions. The original, unprefiltered input signal samples also may be processed to determine improved soft information without the influence of the prefilter to thereby allow the symbol decisions to be refined over and above the result provided by MLSE-PSP.
In a preferred embodiment, the at least one sample for each symbol is one sample for each symbol, and the at least one sample for each symbol is processed by performing Viterbi MLSE and PSP. The at least one sample preferably is prefiltered to thereby condition the at least one sample for the MLSE-PSP processing. The prefilter coefficients may be computed based upon channel coefficients for the communications channel. Channel coefficients may be computed based upon known symbols for the communications channel. The matched filter may include the effect of the receiver intermediate frequency filter.
In combining the block of matched filtered samples and the hard symbol decisions to obtain soft symbol values, the hard symbol decisions may be passed through a composite filter that represents transmit filtering, a multipath propagation channel and the matched filtering. The composite filtering may be based on channel estimates made with the aid of known symbols. The composite filtering also may be based on the hard symbol decisions. The combining may produce one intersymbol interference-free complex number per symbol to provide equalized soft symbol values.
According to another aspect of the present invention, when processing the soft values to obtain soft bit values for each bit of the multibit symbols, the symbols may be rotated through 22.5 degrees such that possible symbol values lie at odd multiples of 22.5 degrees in the complex plane. A soft value for a first bit may be derived only from the real part of the soft symbol values, and a soft value for a second bit may be derived only from the imaginary part of the soft symbol values. The soft value for a third bit per symbol may be derived from the real and imaginary parts of the soft symbol combined with the hard decisions for a first and a second bit. Improved decoding of multibit symbols from a signal that is received on a communications channel thereby may be obtained.